DSCR Based Sensor-Pooling Protocol for Connected Vehicles in Future Smart Cities

Smart cities are racing to create a more connected Intelligent Transportation Systems (ITS) that rely on collecting data from every possible sensor such as a smart utility meter or a smart parking meter. The use of more sensors resulted in generating a lot of information that maps the smart city environment conditions to more real time data points that needed to be shared and analyzed among smart city nodes. One possibility, to carry and share the collected data, is in autonomous vehicles systems, which use the Dedicated Short Range Communications (DSRC) technology. For example, in a Car-to-Parking-Meter or a Vehicle-to-Vehicle (V2V) communications, short-range embedded sensors such as Bluetooth, Cameras, Lidar send the collected data to the vehicle’s Electronic Control Unit (ECU) or to a road side gateway for making collaborative decisions and react to the environment’s surrounding conditions. The goal of this research is to develop and test a DSRC based sensor-pooling protocol for vehicles to cooperatively communicate inclement weather or environment conditions. Five simulation experiments are setup using PreScan and Simulink to validate and study the scalability of the proposed solution. PreScan is an automotive simulation platform that is used for developing and testing Advanced Driver Assistance System (ADAS). The research findings proved that the DSRC can be used to effectively stream the short range sensors’ collected data over a long distance communications link

Cooperative Vehicle Positioning via V2V Communications and Onboard Sensors

Abstract — This paper presents a vehicular positioning system in which multiple vehicles cooperatively calibrate their positions and recognize surrounding vehicles with their GPS receivers and ranging sensors. The proposed system operates in a distributed manner and works even if all vehicles nearby do not or cannot participate in the system. Each vehicle acquires various pieces of positioning information with different degrees of accuracies depending on the sources and recency of information, and compiles them based on likelihood derived from estimated accuracies to minimize estimation errors. A simulation based performance evaluation given in the paper shows that the proposed system improves the estimation accuracy by 85 % on average with respect to the standalone GPS receiver, and recognizes about 70 % surrounding vehicles with an error of 1m

Task-Driven Integrity Assessment and Control for Vehicular Hybrid Localization Systems

Throughout the last decade, vehicle localization has been attracting significant attention in a wide range of applications, including Navigation Systems, Road Tolling, Smart Parking, and Collision Avoidance. To deliver on their requirements, these applications need specific localization accuracy. However, current localization techniques lack the required accuracy, especially for mission critical applications. Although various approaches for improving localization accuracy have been reported in the literature, there is still a need for more efficient and more effective measures that can ascribe some level of accuracy to the localization process. These measures will enable localization systems to manage the localization process and resources so as to achieve the highest accuracy possible, and to mitigate the impact of inadequate accuracy on the target application. In this thesis, a framework for fusing different localization techniques is introduced in order to estimate the location of a vehicle along with location integrity assessment that captures the impact of the measurement conditions on the localization quality. Knowledge about estimate integrity allows the system to plan the use of its localization resources so as to match the target accuracy of the application. The framework introduced provides the tools that would allow for modeling the impact of the operation conditions on estimate accuracy and integrity, as such it enables more robust system performance in three steps. First, localization system parameters are utilized to contrive a feature space that constitutes probable accuracy classes. Due to the strong overlap among accuracy classes in the feature space, a hierarchical classification strategy is developed to address the class ambiguity problem via the class unfolding approach (HCCU). HCCU strategy is proven to be superior with respect to other hierarchical configuration. Furthermore, a Context Based Accuracy Classification (CBAC) algorithm is introduced to enhance the performance of the classification process. In this algorithm, knowledge about the surrounding environment is utilized to optimize classification performance as a function of the observation conditions. Second, a task-driven integrity (TDI) model is developed to enable the applications modules to be aware of the trust level of the localization output. Typically, this trust level functions in the measurement conditions; therefore, the TDI model monitors specific parameter(s) in the localization technique and, accordingly, infers the impact of the change in the environmental conditions on the quality of the localization process. A generalized TDI solution is also introduced to handle the cases where sufficient information about the sensing parameters is unavailable. Finally, the produce of the employed localization techniques (i.e., location estimates, accuracy, and integrity level assessment) needs to be fused. Nevertheless, these techniques are hybrid and their pieces of information are conflicting in many situations. Therefore, a novel evidence structure model called Spatial Evidence Structure Model (SESM) is developed and used in constructing a frame of discernment comprising discretized spatial data. SESM-based fusion paradigms are capable of performing a fusion process using the information provided by the techniques employed. Both the location estimate accuracy and aggregated integrity resultant from the fusion process demonstrate superiority over the employing localization techniques. Furthermore, a context aware task-driven resource allocation mechanism is developed to manage the fusion process. The main objective of this mechanism is to optimize the usage of system resources and achieve a task-driven performance. Extensive experimental work is conducted on real-life and simulated data to validate models developed in this thesis. It is evident from the experimental results that task-driven integrity assessment and control is applicable and effective on hybrid localization systems